{
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  "metadata": {
    "colab": {
      "name": "FinRL_stock_trading_NeurIPS_2018.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "_gDkU-j-fCmZ",
        "3Zpv4S0-fDBv"
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      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.12"
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    "pycharm": {
      "stem_cell": {
        "cell_type": "raw",
        "metadata": {
          "collapsed": false
        },
        "source": []
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AI4Finance-LLC/FinRL/blob/master/FinRL_stock_trading_NeurIPS_2018.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gXaoZs2lh1hi"
      },
      "source": [
        "# Deep Reinforcement Learning for Stock Trading from Scratch: Multiple Stock Trading\n",
        "\n",
        "Tutorials to use OpenAI DRL to trade multiple stocks in one Jupyter Notebook | Presented at NeurIPS 2020: Deep RL Workshop\n",
        "\n",
        "* This blog uses FinRL to reproduce the paper: Practical Deep Reinforcement Learning Approach for Stock Trading, Workshop on Challenges and Opportunities for AI in Financial Services, NeurIPS 2018.\n",
        "* Check out medium blog for detailed explanations: https://towardsdatascience.com/finrl-for-quantitative-finance-tutorial-for-multiple-stock-trading-7b00763b7530\n",
        "* Please report any issues to our Github: https://github.com/AI4Finance-LLC/FinRL-Library/issues\n",
        "* **Pytorch Version** \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGunVt8oLCVS"
      },
      "source": [
        "# Content"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HOzAKQ-SLGX6"
      },
      "source": [
        "* [1. Problem Definition](#0)\n",
        "* [2. Getting Started - Load Python packages](#1)\n",
        "    * [2.1. Install Packages](#1.1)    \n",
        "    * [2.2. Check Additional Packages](#1.2)\n",
        "    * [2.3. Import Packages](#1.3)\n",
        "    * [2.4. Create Folders](#1.4)\n",
        "* [3. Download Data](#2)\n",
        "* [4. Preprocess Data](#3)        \n",
        "    * [4.1. Technical Indicators](#3.1)\n",
        "    * [4.2. Perform Feature Engineering](#3.2)\n",
        "* [5.Build Environment](#4)  \n",
        "    * [5.1. Training & Trade Data Split](#4.1)\n",
        "    * [5.2. User-defined Environment](#4.2)   \n",
        "    * [5.3. Initialize Environment](#4.3)    \n",
        "* [6.Implement DRL Algorithms](#5)  \n",
        "* [7.Backtesting Performance](#6)  \n",
        "    * [7.1. BackTestStats](#6.1)\n",
        "    * [7.2. BackTestPlot](#6.2)   \n",
        "    * [7.3. Baseline Stats](#6.3)   \n",
        "    * [7.3. Compare to Stock Market Index](#6.4)             "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sApkDlD9LIZv"
      },
      "source": [
        "<a id='0'></a>\n",
        "# Part 1. Problem Definition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HjLD2TZSLKZ-"
      },
      "source": [
        "This problem is to design an automated trading solution for single stock trading. We model the stock trading process as a Markov Decision Process (MDP). We then formulate our trading goal as a maximization problem.\n",
        "\n",
        "The algorithm is trained using Deep Reinforcement Learning (DRL) algorithms and the components of the reinforcement learning environment are:\n",
        "\n",
        "\n",
        "* Action: The action space describes the allowed actions that the agent interacts with the\n",
        "environment. Normally, a ∈ A includes three actions: a ∈ {−1, 0, 1}, where −1, 0, 1 represent\n",
        "selling, holding, and buying one stock. Also, an action can be carried upon multiple shares. We use\n",
        "an action space {−k, ..., −1, 0, 1, ..., k}, where k denotes the number of shares. For example, \"Buy\n",
        "10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or −10, respectively\n",
        "\n",
        "* Reward function: r(s, a, s′) is the incentive mechanism for an agent to learn a better action. The change of the portfolio value when action a is taken at state s and arriving at new state s',  i.e., r(s, a, s′) = v′ − v, where v′ and v represent the portfolio\n",
        "values at state s′ and s, respectively\n",
        "\n",
        "* State: The state space describes the observations that the agent receives from the environment. Just as a human trader needs to analyze various information before executing a trade, so\n",
        "our trading agent observes many different features to better learn in an interactive environment.\n",
        "\n",
        "* Environment: Dow 30 consituents\n",
        "\n",
        "\n",
        "The data of the single stock that we will be using for this case study is obtained from Yahoo Finance API. The data contains Open-High-Low-Close price and volume.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ffsre789LY08"
      },
      "source": [
        "<a id='1'></a>\n",
        "# Part 2. Getting Started- Load Python Packages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uy5_PTmOh1hj"
      },
      "source": [
        "<a id='1.1'></a>\n",
        "## 2.1. Install all the packages through FinRL library\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mPT0ipYE28wL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7215141d-23bb-4499-e633-c4fedce6efcd"
      },
      "source": [
        "## install finrl library\n",
        "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.7/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3.6\"->google-auth<2,>=1.6.3->tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0; extra == \"extra\"->stable-baselines3[extra]->finrl==0.3.0) (3.1.1)\n",
            "Building wheels for collected packages: finrl, yfinance, pyfolio, empyrical\n",
            "  Building wheel for finrl (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for finrl: filename=finrl-0.3.0-cp37-none-any.whl size=39043 sha256=2cd551ee388527d7429b005433161d9d5b0989034302f72d4e453d2b99d80435\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-ek00qbko/wheels/9c/19/bf/c644def96612df1ad42c94d5304966797eaa3221dffc5efe0b\n",
            "  Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yfinance: filename=yfinance-0.1.60-py2.py3-none-any.whl size=23819 sha256=42c1f9cbb3d20e95f4c819d6a7d808f6ea739bf61f40dfa7c763f43439b8c057\n",
            "  Stored in directory: /root/.cache/pip/wheels/f0/be/a4/846f02c5985562250917b0ab7b33fff737c8e6e8cd5209aa3b\n",
            "  Building wheel for pyfolio (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyfolio: filename=pyfolio-0.9.2+75.g4b901f6-cp37-none-any.whl size=75776 sha256=9fb70cd3fe15217ec26f9325ba25c6f91a8f6ea2ec236805f8ecd7c3df6d89c6\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-ek00qbko/wheels/43/ce/d9/6752fb6e03205408773235435205a0519d2c608a94f1976e56\n",
            "  Building wheel for empyrical (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for empyrical: filename=empyrical-0.5.5-cp37-none-any.whl size=39780 sha256=3bb1772a947ccdaca7ae3a7f8f25246fab3cb28439b3c3be82d2aae26b9e16d1\n",
            "  Stored in directory: /root/.cache/pip/wheels/ea/b2/c8/6769d8444d2f2e608fae2641833110668d0ffd1abeb2e9f3fc\n",
            "Successfully built finrl yfinance pyfolio empyrical\n",
            "Installing collected packages: int-date, stockstats, lxml, yfinance, stable-baselines3, empyrical, pyfolio, finrl\n",
            "  Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "Successfully installed empyrical-0.5.5 finrl-0.3.0 int-date-0.1.8 lxml-4.6.3 pyfolio-0.9.2+75.g4b901f6 stable-baselines3-1.1.0 stockstats-0.3.2 yfinance-0.1.60\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "osBHhVysOEzi"
      },
      "source": [
        "\n",
        "<a id='1.2'></a>\n",
        "## 2.2. Check if the additional packages needed are present, if not install them. \n",
        "* Yahoo Finance API\n",
        "* pandas\n",
        "* numpy\n",
        "* matplotlib\n",
        "* stockstats\n",
        "* OpenAI gym\n",
        "* stable-baselines\n",
        "* tensorflow\n",
        "* pyfolio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGv01K8Sh1hn"
      },
      "source": [
        "<a id='1.3'></a>\n",
        "## 2.3. Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lPqeTTwoh1hn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e64fdad6-0e53-445b-d503-d35363f8a55b"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "# matplotlib.use('Agg')\n",
        "import datetime\n",
        "\n",
        "%matplotlib inline\n",
        "from finrl.config import config\n",
        "from finrl.marketdata.yahoodownloader import YahooDownloader\n",
        "from finrl.preprocessing.preprocessors import FeatureEngineer\n",
        "from finrl.preprocessing.data import data_split\n",
        "from finrl.env.env_stocktrading import StockTradingEnv\n",
        "from finrl.model.models import DRLAgent\n",
        "from finrl.trade.backtest import backtest_stats, backtest_plot, get_daily_return, get_baseline\n",
        "\n",
        "from pprint import pprint\n",
        "\n",
        "import sys\n",
        "sys.path.append(\"../FinRL-Library\")\n",
        "\n",
        "import itertools"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pyfolio/pos.py:27: UserWarning: Module \"zipline.assets\" not found; multipliers will not be applied to position notionals.\n",
            "  'Module \"zipline.assets\" not found; multipliers will not be applied'\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T2owTj985RW4"
      },
      "source": [
        "<a id='1.4'></a>\n",
        "## 2.4. Create Folders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9A8CN5R5PuZ"
      },
      "source": [
        "import os\n",
        "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
        "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
        "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
        "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
        "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
        "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
        "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
        "    os.makedirs(\"./\" + config.RESULTS_DIR)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A289rQWMh1hq"
      },
      "source": [
        "<a id='2'></a>\n",
        "# Part 3. Download Data\n",
        "Yahoo Finance is a website that provides stock data, financial news, financial reports, etc. All the data provided by Yahoo Finance is free.\n",
        "* FinRL uses a class **YahooDownloader** to fetch data from Yahoo Finance API\n",
        "* Call Limit: Using the Public API (without authentication), you are limited to 2,000 requests per hour per IP (or up to a total of 48,000 requests a day).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NPeQ7iS-LoMm"
      },
      "source": [
        "\n",
        "\n",
        "-----\n",
        "class YahooDownloader:\n",
        "    Provides methods for retrieving daily stock data from\n",
        "    Yahoo Finance API\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "        start_date : str\n",
        "            start date of the data (modified from config.py)\n",
        "        end_date : str\n",
        "            end date of the data (modified from config.py)\n",
        "        ticker_list : list\n",
        "            a list of stock tickers (modified from config.py)\n",
        "\n",
        "    Methods\n",
        "    -------\n",
        "    fetch_data()\n",
        "        Fetches data from yahoo API\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "h3XJnvrbLp-C",
        "outputId": "90ad1901-fda9-46f8-80f6-083cd8d6293a"
      },
      "source": [
        "# from config.py start_date is a string\n",
        "config.START_DATE"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2000-01-01'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "FUnY8WEfLq3C",
        "outputId": "9bd12370-a5e9-4eb3-bc2a-d0d0610f14f7"
      },
      "source": [
        "# from config.py end_date is a string\n",
        "config.END_DATE"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2021-01-01'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzqRRTOX6aFu",
        "outputId": "8acb257f-171e-4069-8ed3-1e7311ffecc2"
      },
      "source": [
        "print(config.DOW_30_TICKER)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['AAPL', 'MSFT', 'JPM', 'V', 'RTX', 'PG', 'GS', 'NKE', 'DIS', 'AXP', 'HD', 'INTC', 'WMT', 'IBM', 'MRK', 'UNH', 'KO', 'CAT', 'TRV', 'JNJ', 'CVX', 'MCD', 'VZ', 'CSCO', 'XOM', 'BA', 'MMM', 'PFE', 'WBA', 'DD']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yCKm4om-s9kE",
        "outputId": "b52a89f3-6f1e-4793-c123-9cd93e8d244d"
      },
      "source": [
        "df = YahooDownloader(start_date = '2009-01-01',\n",
        "                     end_date = '2021-07-06',\n",
        "                     ticker_list = config.DOW_30_TICKER).fetch_data()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (94410, 8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CV3HrZHLh1hy",
        "outputId": "a0fc1d82-5e09-4ddc-b624-24f304db7f79"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(90630, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "4hYkeaPiICHS",
        "outputId": "45c1f6a6-d7ef-4278-f0a6-1182f4c02189"
      },
      "source": [
        "df.sort_values(['date','tic'],ignore_index=True).head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.791740</td>\n",
              "      <td>746015200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.745411</td>\n",
              "      <td>10955700</td>\n",
              "      <td>AXP</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200</td>\n",
              "      <td>BA</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.978760</td>\n",
              "      <td>7117200</td>\n",
              "      <td>CAT</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.683227</td>\n",
              "      <td>40980600</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date       open       high        low      close     volume   tic  day\n",
              "0  2009-01-02   3.067143   3.251429   3.041429   2.791740  746015200  AAPL    4\n",
              "1  2009-01-02  18.570000  19.520000  18.400000  15.745411   10955700   AXP    4\n",
              "2  2009-01-02  42.799999  45.560001  42.779999  33.941101    7010200    BA    4\n",
              "3  2009-01-02  44.910000  46.980000  44.709999  32.978760    7117200   CAT    4\n",
              "4  2009-01-02  16.410000  17.000000  16.250000  12.683227   40980600  CSCO    4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uqC6c40Zh1iH"
      },
      "source": [
        "# Part 4: Preprocess Data\n",
        "Data preprocessing is a crucial step for training a high quality machine learning model. We need to check for missing data and do feature engineering in order to convert the data into a model-ready state.\n",
        "* Add technical indicators. In practical trading, various information needs to be taken into account, for example the historical stock prices, current holding shares, technical indicators, etc. In this article, we demonstrate two trend-following technical indicators: MACD and RSI.\n",
        "* Add turbulence index. Risk-aversion reflects whether an investor will choose to preserve the capital. It also influences one's trading strategy when facing different market volatility level. To control the risk in a worst-case scenario, such as financial crisis of 2007–2008, FinRL employs the financial turbulence index that measures extreme asset price fluctuation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "PmKP-1ii3RLS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81f1e280-d603-422f-98f2-8090969377db"
      },
      "source": [
        "fe = FeatureEngineer(\n",
        "                    use_technical_indicator=True,\n",
        "                    tech_indicator_list = config.TECHNICAL_INDICATORS_LIST,\n",
        "                    use_turbulence=True,\n",
        "                    user_defined_feature = False)\n",
        "\n",
        "processed = fe.preprocess_data(df)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Successfully added technical indicators\n",
            "Successfully added turbulence index\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kixon2tR3RLT"
      },
      "source": [
        "list_ticker = processed[\"tic\"].unique().tolist()\n",
        "list_date = list(pd.date_range(processed['date'].min(),processed['date'].max()).astype(str))\n",
        "combination = list(itertools.product(list_date,list_ticker))\n",
        "\n",
        "processed_full = pd.DataFrame(combination,columns=[\"date\",\"tic\"]).merge(processed,on=[\"date\",\"tic\"],how=\"left\")\n",
        "processed_full = processed_full[processed_full['date'].isin(processed['date'])]\n",
        "processed_full = processed_full.sort_values(['date','tic'])\n",
        "\n",
        "processed_full = processed_full.fillna(0)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "grvhGJJII3Xn",
        "outputId": "0523691f-99d7-4f11-d72f-cf20622f5f28"
      },
      "source": [
        "processed_full.sort_values(['date','tic'],ignore_index=True).head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>day</th>\n",
              "      <th>macd</th>\n",
              "      <th>boll_ub</th>\n",
              "      <th>boll_lb</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>close_30_sma</th>\n",
              "      <th>close_60_sma</th>\n",
              "      <th>turbulence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.791740</td>\n",
              "      <td>746015200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.017278</td>\n",
              "      <td>2.684025</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>2.791740</td>\n",
              "      <td>2.791740</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>AXP</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.745411</td>\n",
              "      <td>10955700.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.002643</td>\n",
              "      <td>3.017278</td>\n",
              "      <td>2.684025</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>2.850651</td>\n",
              "      <td>2.850651</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>BA</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.001880</td>\n",
              "      <td>2.972787</td>\n",
              "      <td>2.735796</td>\n",
              "      <td>70.355711</td>\n",
              "      <td>46.771878</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>2.854292</td>\n",
              "      <td>2.854292</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CAT</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.978760</td>\n",
              "      <td>7117200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-0.000746</td>\n",
              "      <td>2.951725</td>\n",
              "      <td>2.729582</td>\n",
              "      <td>50.429389</td>\n",
              "      <td>-29.777993</td>\n",
              "      <td>43.607834</td>\n",
              "      <td>2.840654</td>\n",
              "      <td>2.840654</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.683227</td>\n",
              "      <td>40980600.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-0.000088</td>\n",
              "      <td>2.939568</td>\n",
              "      <td>2.746169</td>\n",
              "      <td>60.227126</td>\n",
              "      <td>-9.019317</td>\n",
              "      <td>48.357918</td>\n",
              "      <td>2.842869</td>\n",
              "      <td>2.842869</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CVX</td>\n",
              "      <td>74.230003</td>\n",
              "      <td>77.300003</td>\n",
              "      <td>73.580002</td>\n",
              "      <td>47.373642</td>\n",
              "      <td>13695900.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-0.002480</td>\n",
              "      <td>2.931445</td>\n",
              "      <td>2.735506</td>\n",
              "      <td>47.932736</td>\n",
              "      <td>-27.237701</td>\n",
              "      <td>49.952380</td>\n",
              "      <td>2.833476</td>\n",
              "      <td>2.833476</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>DD</td>\n",
              "      <td>21.605234</td>\n",
              "      <td>22.060680</td>\n",
              "      <td>20.993229</td>\n",
              "      <td>15.402899</td>\n",
              "      <td>13251037.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-0.006646</td>\n",
              "      <td>2.938422</td>\n",
              "      <td>2.698235</td>\n",
              "      <td>40.237370</td>\n",
              "      <td>-101.614424</td>\n",
              "      <td>9.320766</td>\n",
              "      <td>2.818329</td>\n",
              "      <td>2.818329</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>DIS</td>\n",
              "      <td>22.760000</td>\n",
              "      <td>24.030001</td>\n",
              "      <td>22.500000</td>\n",
              "      <td>20.597496</td>\n",
              "      <td>9796600.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-0.010724</td>\n",
              "      <td>2.943226</td>\n",
              "      <td>2.663404</td>\n",
              "      <td>37.181826</td>\n",
              "      <td>-113.576183</td>\n",
              "      <td>3.614243</td>\n",
              "      <td>2.803315</td>\n",
              "      <td>2.803315</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>GS</td>\n",
              "      <td>84.019997</td>\n",
              "      <td>87.620003</td>\n",
              "      <td>82.190002</td>\n",
              "      <td>72.844467</td>\n",
              "      <td>14088500.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-0.017016</td>\n",
              "      <td>2.960305</td>\n",
              "      <td>2.606701</td>\n",
              "      <td>31.067739</td>\n",
              "      <td>-136.004971</td>\n",
              "      <td>17.746098</td>\n",
              "      <td>2.783503</td>\n",
              "      <td>2.783503</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>HD</td>\n",
              "      <td>23.070000</td>\n",
              "      <td>24.190001</td>\n",
              "      <td>22.959999</td>\n",
              "      <td>17.909452</td>\n",
              "      <td>14902500.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-0.024322</td>\n",
              "      <td>2.978173</td>\n",
              "      <td>2.545136</td>\n",
              "      <td>27.267345</td>\n",
              "      <td>-166.471239</td>\n",
              "      <td>43.235022</td>\n",
              "      <td>2.761654</td>\n",
              "      <td>2.761654</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date   tic       open  ...  close_30_sma  close_60_sma  turbulence\n",
              "0  2009-01-02  AAPL   3.067143  ...      2.791740      2.791740         0.0\n",
              "1  2009-01-02   AXP  18.570000  ...      2.850651      2.850651         0.0\n",
              "2  2009-01-02    BA  42.799999  ...      2.854292      2.854292         0.0\n",
              "3  2009-01-02   CAT  44.910000  ...      2.840654      2.840654         0.0\n",
              "4  2009-01-02  CSCO  16.410000  ...      2.842869      2.842869         0.0\n",
              "5  2009-01-02   CVX  74.230003  ...      2.833476      2.833476         0.0\n",
              "6  2009-01-02    DD  21.605234  ...      2.818329      2.818329         0.0\n",
              "7  2009-01-02   DIS  22.760000  ...      2.803315      2.803315         0.0\n",
              "8  2009-01-02    GS  84.019997  ...      2.783503      2.783503         0.0\n",
              "9  2009-01-02    HD  23.070000  ...      2.761654      2.761654         0.0\n",
              "\n",
              "[10 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-QsYaY0Dh1iw"
      },
      "source": [
        "<a id='4'></a>\n",
        "# Part 5. Design Environment\n",
        "Considering the stochastic and interactive nature of the automated stock trading tasks, a financial task is modeled as a **Markov Decision Process (MDP)** problem. The training process involves observing stock price change, taking an action and reward's calculation to have the agent adjusting its strategy accordingly. By interacting with the environment, the trading agent will derive a trading strategy with the maximized rewards as time proceeds.\n",
        "\n",
        "Our trading environments, based on OpenAI Gym framework, simulate live stock markets with real market data according to the principle of time-driven simulation.\n",
        "\n",
        "The action space describes the allowed actions that the agent interacts with the environment. Normally, action a includes three actions: {-1, 0, 1}, where -1, 0, 1 represent selling, holding, and buying one share. Also, an action can be carried upon multiple shares. We use an action space {-k,…,-1, 0, 1, …, k}, where k denotes the number of shares to buy and -k denotes the number of shares to sell. For example, \"Buy 10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or -10, respectively. The continuous action space needs to be normalized to [-1, 1], since the policy is defined on a Gaussian distribution, which needs to be normalized and symmetric."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5TOhcryx44bb"
      },
      "source": [
        "## Training data split: 2009-01-01 to 2018-12-31\n",
        "## Trade data split: 2019-01-01 to 2020-09-30"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0qaVGjLtgbI",
        "outputId": "f34e3bf5-f5b8-4f0f-cb78-103441e4a251"
      },
      "source": [
        "train = data_split(processed_full, '2009-01-01','2020-07-01')\n",
        "trade = data_split(processed_full, '2020-07-01','2021-07-06')\n",
        "print(len(train))\n",
        "print(len(trade))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "86790\n",
            "7620\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "p52zNCOhTtLR",
        "outputId": "c3ec2947-2ef7-40c2-cf4c-89320da3d066"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
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              "      <th>low</th>\n",
              "      <th>close</th>\n",
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              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>turbulence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.787006</td>\n",
              "      <td>746015200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>AXP</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.657365</td>\n",
              "      <td>10955700.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>BA</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941101</td>\n",
              "      <td>7010200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CAT</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.830360</td>\n",
              "      <td>7117200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.505757</td>\n",
              "      <td>40980600.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date   tic       open       high  ...  rsi_30     cci_30  dx_30  turbulence\n",
              "0  2009-01-02  AAPL   3.067143   3.251429  ...   100.0  66.666667  100.0         0.0\n",
              "0  2009-01-02   AXP  18.570000  19.520000  ...   100.0  66.666667  100.0         0.0\n",
              "0  2009-01-02    BA  42.799999  45.560001  ...   100.0  66.666667  100.0         0.0\n",
              "0  2009-01-02   CAT  44.910000  46.980000  ...   100.0  66.666667  100.0         0.0\n",
              "0  2009-01-02  CSCO  16.410000  17.000000  ...   100.0  66.666667  100.0         0.0\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        },
        "id": "k9zU9YaTTvFq",
        "outputId": "a449c5b4-b244-44b8-b756-10c82fe9546b"
      },
      "source": [
        "trade.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>open</th>\n",
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              "      <th>boll_ub</th>\n",
              "      <th>boll_lb</th>\n",
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              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
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              "      <th>close_60_sma</th>\n",
              "      <th>turbulence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>38.722500</td>\n",
              "      <td>39.712502</td>\n",
              "      <td>38.557499</td>\n",
              "      <td>38.505024</td>\n",
              "      <td>148158800.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.335191</td>\n",
              "      <td>334.139821</td>\n",
              "      <td>308.258949</td>\n",
              "      <td>47.826590</td>\n",
              "      <td>-27.772146</td>\n",
              "      <td>14.525416</td>\n",
              "      <td>314.202890</td>\n",
              "      <td>312.704339</td>\n",
              "      <td>51.409032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>AXP</td>\n",
              "      <td>93.910004</td>\n",
              "      <td>96.269997</td>\n",
              "      <td>93.769997</td>\n",
              "      <td>92.319603</td>\n",
              "      <td>4175400.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.775741</td>\n",
              "      <td>335.611436</td>\n",
              "      <td>305.851699</td>\n",
              "      <td>46.156688</td>\n",
              "      <td>-67.860390</td>\n",
              "      <td>22.313462</td>\n",
              "      <td>314.159711</td>\n",
              "      <td>312.579659</td>\n",
              "      <td>51.409032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>BA</td>\n",
              "      <td>316.190002</td>\n",
              "      <td>323.950012</td>\n",
              "      <td>313.709991</td>\n",
              "      <td>314.645142</td>\n",
              "      <td>3292200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.338838</td>\n",
              "      <td>336.485023</td>\n",
              "      <td>304.296566</td>\n",
              "      <td>46.806048</td>\n",
              "      <td>-72.067253</td>\n",
              "      <td>24.597730</td>\n",
              "      <td>314.064055</td>\n",
              "      <td>312.452928</td>\n",
              "      <td>51.409032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>CAT</td>\n",
              "      <td>124.029999</td>\n",
              "      <td>127.879997</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>118.671188</td>\n",
              "      <td>4783200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.655001</td>\n",
              "      <td>336.668731</td>\n",
              "      <td>303.510025</td>\n",
              "      <td>49.792068</td>\n",
              "      <td>-33.729612</td>\n",
              "      <td>14.054906</td>\n",
              "      <td>314.492846</td>\n",
              "      <td>312.392662</td>\n",
              "      <td>51.409032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>42.279999</td>\n",
              "      <td>43.200001</td>\n",
              "      <td>42.209999</td>\n",
              "      <td>40.057236</td>\n",
              "      <td>23833500.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.159995</td>\n",
              "      <td>336.703986</td>\n",
              "      <td>304.162298</td>\n",
              "      <td>53.389344</td>\n",
              "      <td>31.217425</td>\n",
              "      <td>7.135987</td>\n",
              "      <td>315.392609</td>\n",
              "      <td>312.451774</td>\n",
              "      <td>51.409032</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date   tic        open  ...  close_30_sma  close_60_sma  turbulence\n",
              "0  2019-01-02  AAPL   38.722500  ...    314.202890    312.704339   51.409032\n",
              "0  2019-01-02   AXP   93.910004  ...    314.159711    312.579659   51.409032\n",
              "0  2019-01-02    BA  316.190002  ...    314.064055    312.452928   51.409032\n",
              "0  2019-01-02   CAT  124.029999  ...    314.492846    312.392662   51.409032\n",
              "0  2019-01-02  CSCO   42.279999  ...    315.392609    312.451774   51.409032\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zYN573SOHhxG",
        "outputId": "73e2c676-d636-47a3-c956-7c32541be416"
      },
      "source": [
        "config.TECHNICAL_INDICATORS_LIST"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['macd', 'rsi_30', 'cci_30', 'dx_30']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2zqII8rMIqn",
        "outputId": "78ea7562-93b2-4cd5-eafb-37f6a834cf64"
      },
      "source": [
        "stock_dimension = len(train.tic.unique())\n",
        "state_space = 1 + 2*stock_dimension + len(config.TECHNICAL_INDICATORS_LIST)*stock_dimension\n",
        "print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Stock Dimension: 30, State Space: 181\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AWyp84Ltto19"
      },
      "source": [
        "env_kwargs = {\n",
        "    \"hmax\": 100, \n",
        "    \"initial_amount\": 1000000, \n",
        "    \"buy_cost_pct\": 0.001,\n",
        "    \"sell_cost_pct\": 0.001,\n",
        "    \"state_space\": state_space, \n",
        "    \"stock_dim\": stock_dimension, \n",
        "    \"tech_indicator_list\": config.TECHNICAL_INDICATORS_LIST, \n",
        "    \"action_space\": stock_dimension, \n",
        "    \"reward_scaling\": 1e-4\n",
        "    \n",
        "}\n",
        "\n",
        "e_train_gym = StockTradingEnv(df = train, **env_kwargs)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "64EoqOrQjiVf"
      },
      "source": [
        "## Environment for Training\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwSvvPjutpqS",
        "outputId": "5af67207-364b-4947-daee-3c207a042a66"
      },
      "source": [
        "env_train, _ = e_train_gym.get_sb_env()\n",
        "print(type(env_train))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'stable_baselines3.common.vec_env.dummy_vec_env.DummyVecEnv'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMNR5nHjh1iz"
      },
      "source": [
        "<a id='5'></a>\n",
        "# Part 6: Implement DRL Algorithms\n",
        "* The implementation of the DRL algorithms are based on **OpenAI Baselines** and **Stable Baselines**. Stable Baselines is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups.\n",
        "* FinRL library includes fine-tuned standard DRL algorithms, such as DQN, DDPG,\n",
        "Multi-Agent DDPG, PPO, SAC, A2C and TD3. We also allow users to\n",
        "design their own DRL algorithms by adapting these DRL algorithms."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "364PsqckttcQ"
      },
      "source": [
        "agent = DRLAgent(env = env_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YDmqOyF9h1iz"
      },
      "source": [
        "### Model Training: 5 models, A2C DDPG, PPO, TD3, SAC\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uijiWgkuh1jB"
      },
      "source": [
        "### Model 1: A2C\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GUCnkn-HIbmj",
        "outputId": "c5059c77-d971-447b-b703-cb3cc51b52ac"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "model_a2c = agent.get_model(\"a2c\")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0007}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0GVpkWGqH4-D",
        "outputId": "24b01293-0d40-4c51-9ec1-12542322e354"
      },
      "source": [
        "trained_a2c = agent.train_model(model=model_a2c, \n",
        "                             tb_log_name='a2c',\n",
        "                             total_timesteps=50000)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/a2c/a2c_1\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 75       |\n",
            "|    iterations         | 100      |\n",
            "|    time_elapsed       | 6        |\n",
            "|    total_timesteps    | 500      |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0.0625   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 99       |\n",
            "|    policy_loss        | -68.4    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 3.25     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 200      |\n",
            "|    time_elapsed       | 11       |\n",
            "|    total_timesteps    | 1000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 199      |\n",
            "|    policy_loss        | -112     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 8.37     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 90       |\n",
            "|    iterations         | 300      |\n",
            "|    time_elapsed       | 16       |\n",
            "|    total_timesteps    | 1500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 299      |\n",
            "|    policy_loss        | -11.4    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 4.56     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 92       |\n",
            "|    iterations         | 400      |\n",
            "|    time_elapsed       | 21       |\n",
            "|    total_timesteps    | 2000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0.0481   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 399      |\n",
            "|    policy_loss        | -17.5    |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 1.37     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 93       |\n",
            "|    iterations         | 500      |\n",
            "|    time_elapsed       | 26       |\n",
            "|    total_timesteps    | 2500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 499      |\n",
            "|    policy_loss        | 621      |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 343      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 95       |\n",
            "|    iterations         | 600      |\n",
            "|    time_elapsed       | 31       |\n",
            "|    total_timesteps    | 3000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.6    |\n",
            "|    explained_variance | -0.045   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 599      |\n",
            "|    policy_loss        | 202      |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 20.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 700      |\n",
            "|    time_elapsed       | 36       |\n",
            "|    total_timesteps    | 3500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 699      |\n",
            "|    policy_loss        | -111     |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 7.1      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 800      |\n",
            "|    time_elapsed       | 41       |\n",
            "|    total_timesteps    | 4000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -0.0069  |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 799      |\n",
            "|    policy_loss        | 171      |\n",
            "|    std                | 1        |\n",
            "|    value_loss         | 18.5     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 89       |\n",
            "|    iterations         | 900      |\n",
            "|    time_elapsed       | 50       |\n",
            "|    total_timesteps    | 4500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 899      |\n",
            "|    policy_loss        | 160      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 14       |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 80       |\n",
            "|    iterations         | 1000     |\n",
            "|    time_elapsed       | 62       |\n",
            "|    total_timesteps    | 5000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 999      |\n",
            "|    policy_loss        | 3.64     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 1.02     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 82       |\n",
            "|    iterations         | 1100     |\n",
            "|    time_elapsed       | 66       |\n",
            "|    total_timesteps    | 5500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1099     |\n",
            "|    policy_loss        | -684     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 245      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 83       |\n",
            "|    iterations         | 1200     |\n",
            "|    time_elapsed       | 71       |\n",
            "|    total_timesteps    | 6000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | -0.126   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1199     |\n",
            "|    policy_loss        | -62.5    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 3.11     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 84       |\n",
            "|    iterations         | 1300     |\n",
            "|    time_elapsed       | 76       |\n",
            "|    total_timesteps    | 6500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0.0535   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1299     |\n",
            "|    policy_loss        | 25.1     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 3.8      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 85       |\n",
            "|    iterations         | 1400     |\n",
            "|    time_elapsed       | 81       |\n",
            "|    total_timesteps    | 7000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | -0.0791  |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1399     |\n",
            "|    policy_loss        | 31.3     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 2.85     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 86       |\n",
            "|    iterations         | 1500     |\n",
            "|    time_elapsed       | 86       |\n",
            "|    total_timesteps    | 7500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1499     |\n",
            "|    policy_loss        | -50.4    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 16.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 87       |\n",
            "|    iterations         | 1600     |\n",
            "|    time_elapsed       | 91       |\n",
            "|    total_timesteps    | 8000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1599     |\n",
            "|    policy_loss        | 235      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 38.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 88       |\n",
            "|    iterations         | 1700     |\n",
            "|    time_elapsed       | 96       |\n",
            "|    total_timesteps    | 8500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0.0378   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1699     |\n",
            "|    policy_loss        | -34      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 34.2     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 88       |\n",
            "|    iterations         | 1800     |\n",
            "|    time_elapsed       | 101      |\n",
            "|    total_timesteps    | 9000     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1799     |\n",
            "|    policy_loss        | -21.4    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.358    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 89       |\n",
            "|    iterations         | 1900     |\n",
            "|    time_elapsed       | 106      |\n",
            "|    total_timesteps    | 9500     |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0.269    |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1899     |\n",
            "|    policy_loss        | 56.3     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 2.68     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 89       |\n",
            "|    iterations         | 2000     |\n",
            "|    time_elapsed       | 111      |\n",
            "|    total_timesteps    | 10000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 1999     |\n",
            "|    policy_loss        | -32.9    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 1.38     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 90       |\n",
            "|    iterations         | 2100     |\n",
            "|    time_elapsed       | 116      |\n",
            "|    total_timesteps    | 10500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | -0.372   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2099     |\n",
            "|    policy_loss        | 83.3     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 11.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 90       |\n",
            "|    iterations         | 2200     |\n",
            "|    time_elapsed       | 121      |\n",
            "|    total_timesteps    | 11000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | -0.162   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2199     |\n",
            "|    policy_loss        | 16.1     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 7.65     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 90        |\n",
            "|    iterations         | 2300      |\n",
            "|    time_elapsed       | 126       |\n",
            "|    total_timesteps    | 11500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.9     |\n",
            "|    explained_variance | -0.0149   |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 2299      |\n",
            "|    policy_loss        | -3.33e+03 |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 6.78e+03  |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 91       |\n",
            "|    iterations         | 2400     |\n",
            "|    time_elapsed       | 131      |\n",
            "|    total_timesteps    | 12000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | -0.00234 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2399     |\n",
            "|    policy_loss        | 51.7     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 4.26     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 91       |\n",
            "|    iterations         | 2500     |\n",
            "|    time_elapsed       | 136      |\n",
            "|    total_timesteps    | 12500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0.0849   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2499     |\n",
            "|    policy_loss        | -99.8    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 5.8      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 92       |\n",
            "|    iterations         | 2600     |\n",
            "|    time_elapsed       | 141      |\n",
            "|    total_timesteps    | 13000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0.172    |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2599     |\n",
            "|    policy_loss        | 41.5     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 1.49     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 92        |\n",
            "|    iterations         | 2700      |\n",
            "|    time_elapsed       | 146       |\n",
            "|    total_timesteps    | 13500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.8     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 2699      |\n",
            "|    policy_loss        | -45.7     |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 2.82      |\n",
            "-------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 92        |\n",
            "|    iterations         | 2800      |\n",
            "|    time_elapsed       | 150       |\n",
            "|    total_timesteps    | 14000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.9     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 2799      |\n",
            "|    policy_loss        | 306       |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 44.8      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 93       |\n",
            "|    iterations         | 2900     |\n",
            "|    time_elapsed       | 155      |\n",
            "|    total_timesteps    | 14500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0.000542 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2899     |\n",
            "|    policy_loss        | -207     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 25.6     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 93       |\n",
            "|    iterations         | 3000     |\n",
            "|    time_elapsed       | 160      |\n",
            "|    total_timesteps    | 15000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 1.72e-05 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 2999     |\n",
            "|    policy_loss        | 17.3     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 0.839    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 93       |\n",
            "|    iterations         | 3100     |\n",
            "|    time_elapsed       | 165      |\n",
            "|    total_timesteps    | 15500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3099     |\n",
            "|    policy_loss        | 47.9     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 2.3      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 93       |\n",
            "|    iterations         | 3200     |\n",
            "|    time_elapsed       | 170      |\n",
            "|    total_timesteps    | 16000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0.0901   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3199     |\n",
            "|    policy_loss        | -75.4    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 14.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 93       |\n",
            "|    iterations         | 3300     |\n",
            "|    time_elapsed       | 175      |\n",
            "|    total_timesteps    | 16500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3299     |\n",
            "|    policy_loss        | -65.9    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 3.68     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 94        |\n",
            "|    iterations         | 3400      |\n",
            "|    time_elapsed       | 180       |\n",
            "|    total_timesteps    | 17000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -42.8     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 3399      |\n",
            "|    policy_loss        | 61.2      |\n",
            "|    std                | 1.01      |\n",
            "|    value_loss         | 3.61      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 94       |\n",
            "|    iterations         | 3500     |\n",
            "|    time_elapsed       | 185      |\n",
            "|    total_timesteps    | 17500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | -0.0122  |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3499     |\n",
            "|    policy_loss        | 175      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 25.1     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 94       |\n",
            "|    iterations         | 3600     |\n",
            "|    time_elapsed       | 190      |\n",
            "|    total_timesteps    | 18000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3599     |\n",
            "|    policy_loss        | -93.9    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 8.05     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 94       |\n",
            "|    iterations         | 3700     |\n",
            "|    time_elapsed       | 195      |\n",
            "|    total_timesteps    | 18500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.7    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3699     |\n",
            "|    policy_loss        | 166      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 20.3     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 95       |\n",
            "|    iterations         | 3800     |\n",
            "|    time_elapsed       | 199      |\n",
            "|    total_timesteps    | 19000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3799     |\n",
            "|    policy_loss        | 206      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 20       |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 95       |\n",
            "|    iterations         | 3900     |\n",
            "|    time_elapsed       | 204      |\n",
            "|    total_timesteps    | 19500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3899     |\n",
            "|    policy_loss        | -159     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 13.2     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 95       |\n",
            "|    iterations         | 4000     |\n",
            "|    time_elapsed       | 209      |\n",
            "|    total_timesteps    | 20000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 3999     |\n",
            "|    policy_loss        | -238     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 32.3     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 95       |\n",
            "|    iterations         | 4100     |\n",
            "|    time_elapsed       | 214      |\n",
            "|    total_timesteps    | 20500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4099     |\n",
            "|    policy_loss        | -30.5    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 2.28     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 95       |\n",
            "|    iterations         | 4200     |\n",
            "|    time_elapsed       | 219      |\n",
            "|    total_timesteps    | 21000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4199     |\n",
            "|    policy_loss        | -107     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 8.08     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 95       |\n",
            "|    iterations         | 4300     |\n",
            "|    time_elapsed       | 224      |\n",
            "|    total_timesteps    | 21500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.8    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4299     |\n",
            "|    policy_loss        | -37.2    |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 2.94     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 95       |\n",
            "|    iterations         | 4400     |\n",
            "|    time_elapsed       | 229      |\n",
            "|    total_timesteps    | 22000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4399     |\n",
            "|    policy_loss        | 77.2     |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 7.8      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 4500     |\n",
            "|    time_elapsed       | 234      |\n",
            "|    total_timesteps    | 22500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4499     |\n",
            "|    policy_loss        | 149      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 10.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 4600     |\n",
            "|    time_elapsed       | 239      |\n",
            "|    total_timesteps    | 23000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -42.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4599     |\n",
            "|    policy_loss        | 166      |\n",
            "|    std                | 1.01     |\n",
            "|    value_loss         | 15.1     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 96        |\n",
            "|    iterations         | 4700      |\n",
            "|    time_elapsed       | 243       |\n",
            "|    total_timesteps    | 23500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -43       |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 4699      |\n",
            "|    policy_loss        | -138      |\n",
            "|    std                | 1.02      |\n",
            "|    value_loss         | 13.7      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 4800     |\n",
            "|    time_elapsed       | 248      |\n",
            "|    total_timesteps    | 24000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | 0.101    |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4799     |\n",
            "|    policy_loss        | -168     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 16.9     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 4900     |\n",
            "|    time_elapsed       | 253      |\n",
            "|    total_timesteps    | 24500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4899     |\n",
            "|    policy_loss        | -54.6    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 2.94     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 5000     |\n",
            "|    time_elapsed       | 258      |\n",
            "|    total_timesteps    | 25000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 4999     |\n",
            "|    policy_loss        | -10.4    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 1.57     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 5100     |\n",
            "|    time_elapsed       | 263      |\n",
            "|    total_timesteps    | 25500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43      |\n",
            "|    explained_variance | -0.0426  |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5099     |\n",
            "|    policy_loss        | -111     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 17.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 96       |\n",
            "|    iterations         | 5200     |\n",
            "|    time_elapsed       | 268      |\n",
            "|    total_timesteps    | 26000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5199     |\n",
            "|    policy_loss        | -270     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 48.8     |\n",
            "------------------------------------\n",
            "day: 2892, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3351232.53\n",
            "total_reward: 2351232.53\n",
            "total_cost: 9537.16\n",
            "total_trades: 45521\n",
            "Sharpe: 0.709\n",
            "=================================\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 5300     |\n",
            "|    time_elapsed       | 273      |\n",
            "|    total_timesteps    | 26500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5299     |\n",
            "|    policy_loss        | -88.7    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 3.79     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 5400     |\n",
            "|    time_elapsed       | 278      |\n",
            "|    total_timesteps    | 27000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5399     |\n",
            "|    policy_loss        | -116     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 8.55     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 5500     |\n",
            "|    time_elapsed       | 283      |\n",
            "|    total_timesteps    | 27500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0.00603  |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5499     |\n",
            "|    policy_loss        | -19.6    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 11.9     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 5600     |\n",
            "|    time_elapsed       | 287      |\n",
            "|    total_timesteps    | 28000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5599     |\n",
            "|    policy_loss        | -81.3    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 6.64     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 5700     |\n",
            "|    time_elapsed       | 292      |\n",
            "|    total_timesteps    | 28500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5699     |\n",
            "|    policy_loss        | 69.1     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 4.83     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 5800     |\n",
            "|    time_elapsed       | 297      |\n",
            "|    total_timesteps    | 29000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.2    |\n",
            "|    explained_variance | 0.00844  |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5799     |\n",
            "|    policy_loss        | -40.8    |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 4.9      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 5900     |\n",
            "|    time_elapsed       | 302      |\n",
            "|    total_timesteps    | 29500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5899     |\n",
            "|    policy_loss        | 66.6     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 3.52     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 6000     |\n",
            "|    time_elapsed       | 307      |\n",
            "|    total_timesteps    | 30000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.2    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 5999     |\n",
            "|    policy_loss        | 68.1     |\n",
            "|    std                | 1.02     |\n",
            "|    value_loss         | 3.34     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 97        |\n",
            "|    iterations         | 6100      |\n",
            "|    time_elapsed       | 312       |\n",
            "|    total_timesteps    | 30500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -43.2     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 6099      |\n",
            "|    policy_loss        | -0.969    |\n",
            "|    std                | 1.02      |\n",
            "|    value_loss         | 2.05      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 6200     |\n",
            "|    time_elapsed       | 317      |\n",
            "|    total_timesteps    | 31000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6199     |\n",
            "|    policy_loss        | -39.7    |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 1.61     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 6300     |\n",
            "|    time_elapsed       | 322      |\n",
            "|    total_timesteps    | 31500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6299     |\n",
            "|    policy_loss        | 295      |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 73.7     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 6400     |\n",
            "|    time_elapsed       | 327      |\n",
            "|    total_timesteps    | 32000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.3    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6399     |\n",
            "|    policy_loss        | 62.4     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 2.79     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 6500     |\n",
            "|    time_elapsed       | 332      |\n",
            "|    total_timesteps    | 32500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.3    |\n",
            "|    explained_variance | 0.0479   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6499     |\n",
            "|    policy_loss        | 43.9     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 8.15     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 6600     |\n",
            "|    time_elapsed       | 337      |\n",
            "|    total_timesteps    | 33000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.4    |\n",
            "|    explained_variance | 5.96e-08 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6599     |\n",
            "|    policy_loss        | -63.7    |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 3.49     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 97       |\n",
            "|    iterations         | 6700     |\n",
            "|    time_elapsed       | 341      |\n",
            "|    total_timesteps    | 33500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6699     |\n",
            "|    policy_loss        | -630     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 263      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 6800     |\n",
            "|    time_elapsed       | 346      |\n",
            "|    total_timesteps    | 34000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6799     |\n",
            "|    policy_loss        | -113     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 7.69     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 6900     |\n",
            "|    time_elapsed       | 351      |\n",
            "|    total_timesteps    | 34500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6899     |\n",
            "|    policy_loss        | 66.5     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 8.11     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 7000     |\n",
            "|    time_elapsed       | 356      |\n",
            "|    total_timesteps    | 35000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.4    |\n",
            "|    explained_variance | 0.0065   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 6999     |\n",
            "|    policy_loss        | 71.7     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 3.41     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 7100     |\n",
            "|    time_elapsed       | 361      |\n",
            "|    total_timesteps    | 35500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7099     |\n",
            "|    policy_loss        | -14.9    |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 0.289    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 7200     |\n",
            "|    time_elapsed       | 366      |\n",
            "|    total_timesteps    | 36000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.4    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7199     |\n",
            "|    policy_loss        | -229     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 29.3     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 7300     |\n",
            "|    time_elapsed       | 371      |\n",
            "|    total_timesteps    | 36500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.5    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7299     |\n",
            "|    policy_loss        | 52.9     |\n",
            "|    std                | 1.03     |\n",
            "|    value_loss         | 2.42     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 7400     |\n",
            "|    time_elapsed       | 376      |\n",
            "|    total_timesteps    | 37000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7399     |\n",
            "|    policy_loss        | 108      |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 8.4      |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 7500     |\n",
            "|    time_elapsed       | 381      |\n",
            "|    total_timesteps    | 37500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7499     |\n",
            "|    policy_loss        | 213      |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 29.7     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 7600      |\n",
            "|    time_elapsed       | 386       |\n",
            "|    total_timesteps    | 38000     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -43.6     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 7599      |\n",
            "|    policy_loss        | -81.1     |\n",
            "|    std                | 1.04      |\n",
            "|    value_loss         | 3.67      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 7700     |\n",
            "|    time_elapsed       | 391      |\n",
            "|    total_timesteps    | 38500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7699     |\n",
            "|    policy_loss        | -82.9    |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 4.69     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 7800     |\n",
            "|    time_elapsed       | 395      |\n",
            "|    total_timesteps    | 39000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7799     |\n",
            "|    policy_loss        | -86.2    |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 4.55     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 7900     |\n",
            "|    time_elapsed       | 400      |\n",
            "|    total_timesteps    | 39500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7899     |\n",
            "|    policy_loss        | 248      |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 40.2     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 8000     |\n",
            "|    time_elapsed       | 405      |\n",
            "|    total_timesteps    | 40000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.6    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 7999     |\n",
            "|    policy_loss        | -45.6    |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 2.15     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 8100      |\n",
            "|    time_elapsed       | 410       |\n",
            "|    total_timesteps    | 40500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -43.7     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 8099      |\n",
            "|    policy_loss        | -208      |\n",
            "|    std                | 1.04      |\n",
            "|    value_loss         | 29        |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 8200     |\n",
            "|    time_elapsed       | 415      |\n",
            "|    total_timesteps    | 41000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.7    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8199     |\n",
            "|    policy_loss        | 8.25     |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 0.189    |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 8300     |\n",
            "|    time_elapsed       | 420      |\n",
            "|    total_timesteps    | 41500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8299     |\n",
            "|    policy_loss        | 36.7     |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 1.22     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 8400     |\n",
            "|    time_elapsed       | 425      |\n",
            "|    total_timesteps    | 42000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.8    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8399     |\n",
            "|    policy_loss        | -171     |\n",
            "|    std                | 1.04     |\n",
            "|    value_loss         | 18.5     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 8500     |\n",
            "|    time_elapsed       | 429      |\n",
            "|    total_timesteps    | 42500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.8    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8499     |\n",
            "|    policy_loss        | 2.04     |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 0.53     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 8600     |\n",
            "|    time_elapsed       | 434      |\n",
            "|    total_timesteps    | 43000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8599     |\n",
            "|    policy_loss        | -31.1    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 11.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 8700     |\n",
            "|    time_elapsed       | 439      |\n",
            "|    total_timesteps    | 43500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.8    |\n",
            "|    explained_variance | 0.00995  |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8699     |\n",
            "|    policy_loss        | -15.2    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 1.29     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 98       |\n",
            "|    iterations         | 8800     |\n",
            "|    time_elapsed       | 444      |\n",
            "|    total_timesteps    | 44000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -43.9    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8799     |\n",
            "|    policy_loss        | -0.748   |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 0.995    |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 98        |\n",
            "|    iterations         | 8900      |\n",
            "|    time_elapsed       | 449       |\n",
            "|    total_timesteps    | 44500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -43.9     |\n",
            "|    explained_variance | -1.19e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 8899      |\n",
            "|    policy_loss        | 48.5      |\n",
            "|    std                | 1.05      |\n",
            "|    value_loss         | 2.16      |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 99       |\n",
            "|    iterations         | 9000     |\n",
            "|    time_elapsed       | 454      |\n",
            "|    total_timesteps    | 45000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44      |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 8999     |\n",
            "|    policy_loss        | -68.5    |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 3.76     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 99       |\n",
            "|    iterations         | 9100     |\n",
            "|    time_elapsed       | 459      |\n",
            "|    total_timesteps    | 45500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9099     |\n",
            "|    policy_loss        | 62.6     |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 2.33     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 99       |\n",
            "|    iterations         | 9200     |\n",
            "|    time_elapsed       | 464      |\n",
            "|    total_timesteps    | 46000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9199     |\n",
            "|    policy_loss        | -158     |\n",
            "|    std                | 1.05     |\n",
            "|    value_loss         | 18.5     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 99       |\n",
            "|    iterations         | 9300     |\n",
            "|    time_elapsed       | 469      |\n",
            "|    total_timesteps    | 46500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.1    |\n",
            "|    explained_variance | 0.0152   |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9299     |\n",
            "|    policy_loss        | -58.6    |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 2.38     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 99       |\n",
            "|    iterations         | 9400     |\n",
            "|    time_elapsed       | 474      |\n",
            "|    total_timesteps    | 47000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.1    |\n",
            "|    explained_variance | 1.79e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9399     |\n",
            "|    policy_loss        | 128      |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 10.4     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 99       |\n",
            "|    iterations         | 9500     |\n",
            "|    time_elapsed       | 478      |\n",
            "|    total_timesteps    | 47500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9499     |\n",
            "|    policy_loss        | -58.5    |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 3.39     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 99       |\n",
            "|    iterations         | 9600     |\n",
            "|    time_elapsed       | 483      |\n",
            "|    total_timesteps    | 48000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9599     |\n",
            "|    policy_loss        | -18.1    |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 1.04     |\n",
            "------------------------------------\n",
            "-------------------------------------\n",
            "| time/                 |           |\n",
            "|    fps                | 99        |\n",
            "|    iterations         | 9700      |\n",
            "|    time_elapsed       | 488       |\n",
            "|    total_timesteps    | 48500     |\n",
            "| train/                |           |\n",
            "|    entropy_loss       | -44.2     |\n",
            "|    explained_variance | -2.38e-07 |\n",
            "|    learning_rate      | 0.0007    |\n",
            "|    n_updates          | 9699      |\n",
            "|    policy_loss        | 30.3      |\n",
            "|    std                | 1.06      |\n",
            "|    value_loss         | 0.783     |\n",
            "-------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 99       |\n",
            "|    iterations         | 9800     |\n",
            "|    time_elapsed       | 493      |\n",
            "|    total_timesteps    | 49000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9799     |\n",
            "|    policy_loss        | -18.2    |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 22.2     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 99       |\n",
            "|    iterations         | 9900     |\n",
            "|    time_elapsed       | 498      |\n",
            "|    total_timesteps    | 49500    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.1    |\n",
            "|    explained_variance | 0        |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9899     |\n",
            "|    policy_loss        | 25.5     |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 0.43     |\n",
            "------------------------------------\n",
            "------------------------------------\n",
            "| time/                 |          |\n",
            "|    fps                | 99       |\n",
            "|    iterations         | 10000    |\n",
            "|    time_elapsed       | 503      |\n",
            "|    total_timesteps    | 50000    |\n",
            "| train/                |          |\n",
            "|    entropy_loss       | -44.1    |\n",
            "|    explained_variance | 1.19e-07 |\n",
            "|    learning_rate      | 0.0007   |\n",
            "|    n_updates          | 9999     |\n",
            "|    policy_loss        | 24       |\n",
            "|    std                | 1.06     |\n",
            "|    value_loss         | 1.66     |\n",
            "------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRiOtrywfAo1"
      },
      "source": [
        "### Model 2: DDPG"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M2YadjfnLwgt",
        "outputId": "3b2a8f89-0561-4083-a015-fbee11693037"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "model_ddpg = agent.get_model(\"ddpg\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 128, 'buffer_size': 50000, 'learning_rate': 0.001}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "tCDa78rqfO_a",
        "jupyter": {
          "outputs_hidden": true
        },
        "outputId": "f651f8be-4c93-4b1e-c88a-7e3a09976693"
      },
      "source": [
        "trained_ddpg = agent.train_model(model=model_ddpg, \n",
        "                             tb_log_name='ddpg',\n",
        "                             total_timesteps=50000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/ddpg/ddpg_1\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3761309.8057632465\n",
            "total_reward:2761309.8057632465\n",
            "total_cost:  6807.077776350557\n",
            "total_trades:  39070\n",
            "Sharpe:  1.0173492167488003\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 4        |\n",
            "|    fps             | 38       |\n",
            "|    time_elapsed    | 258      |\n",
            "|    total timesteps | 10064    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -2.81    |\n",
            "|    critic_loss     | 272      |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 7548     |\n",
            "---------------------------------\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 8        |\n",
            "|    fps             | 33       |\n",
            "|    time_elapsed    | 604      |\n",
            "|    total timesteps | 20128    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -8.32    |\n",
            "|    critic_loss     | 12.8     |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 17612    |\n",
            "---------------------------------\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 12       |\n",
            "|    fps             | 31       |\n",
            "|    time_elapsed    | 953      |\n",
            "|    total timesteps | 30192    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -9.46    |\n",
            "|    critic_loss     | 4.31     |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 27676    |\n",
            "---------------------------------\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 16       |\n",
            "|    fps             | 30       |\n",
            "|    time_elapsed    | 1309     |\n",
            "|    total timesteps | 40256    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -10.2    |\n",
            "|    critic_loss     | 3.19     |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 37740    |\n",
            "---------------------------------\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4423657.61673363\n",
            "total_reward:3423657.61673363\n",
            "total_cost:  1277.392035166502\n",
            "total_trades:  32819\n",
            "Sharpe:  0.8726982452731067\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 20       |\n",
            "|    fps             | 30       |\n",
            "|    time_elapsed    | 1675     |\n",
            "|    total timesteps | 50320    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | -11.1    |\n",
            "|    critic_loss     | 2.24     |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 47804    |\n",
            "---------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_gDkU-j-fCmZ"
      },
      "source": [
        "### Model 3: PPO"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y5D5PFUhMzSV",
        "outputId": "1bd9a8e4-b094-4649-b0af-d6ae6562221f"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "PPO_PARAMS = {\n",
        "    \"n_steps\": 2048,\n",
        "    \"ent_coef\": 0.01,\n",
        "    \"learning_rate\": 0.00025,\n",
        "    \"batch_size\": 128,\n",
        "}\n",
        "model_ppo = agent.get_model(\"ppo\",model_kwargs = PPO_PARAMS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'n_steps': 2048, 'ent_coef': 0.01, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "collapsed": true,
        "id": "Gt8eIQKYM4G3",
        "jupyter": {
          "outputs_hidden": true
        },
        "outputId": "f0cdbe05-8c27-4c20-bd5e-a34c7c5923b0"
      },
      "source": [
        "trained_ppo = agent.train_model(model=model_ppo, \n",
        "                             tb_log_name='ppo',\n",
        "                             total_timesteps=50000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/ppo/ppo_2\n",
            "-----------------------------\n",
            "| time/              |      |\n",
            "|    fps             | 107  |\n",
            "|    iterations      | 1    |\n",
            "|    time_elapsed    | 19   |\n",
            "|    total_timesteps | 2048 |\n",
            "-----------------------------\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-58-c744e952963e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m trained_ppo = agent.train_model(model=model_ppo, \n\u001b[1;32m      2\u001b[0m                              \u001b[0mtb_log_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ppo'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m                              total_timesteps=50000)\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/finrl/model/models.py\u001b[0m in \u001b[0;36mtrain_model\u001b[0;34m(self, model, tb_log_name, total_timesteps)\u001b[0m\n\u001b[1;32m    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtrain_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_log_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 124\u001b[0;31m         \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_log_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtb_log_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    125\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/ppo/ppo.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, total_timesteps, callback, log_interval, eval_env, eval_freq, n_eval_episodes, tb_log_name, eval_log_path, reset_num_timesteps)\u001b[0m\n\u001b[1;32m    262\u001b[0m             \u001b[0mtb_log_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtb_log_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m             \u001b[0meval_log_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0meval_log_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 264\u001b[0;31m             \u001b[0mreset_num_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreset_num_timesteps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    265\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/on_policy_algorithm.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, total_timesteps, callback, log_interval, eval_env, eval_freq, n_eval_episodes, tb_log_name, eval_log_path, reset_num_timesteps)\u001b[0m\n\u001b[1;32m    220\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_timesteps\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m             \u001b[0mcontinue_training\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcollect_rollouts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrollout_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_rollout_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcontinue_training\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/on_policy_algorithm.py\u001b[0m in \u001b[0;36mcollect_rollouts\u001b[0;34m(self, env, callback, rollout_buffer, n_rollout_steps)\u001b[0m\n\u001b[1;32m    161\u001b[0m                 \u001b[0mclipped_actions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maction_space\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maction_space\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhigh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m             \u001b[0mnew_obs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrewards\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdones\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclipped_actions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_timesteps\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_envs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/vec_env/base_vec_env.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, actions)\u001b[0m\n\u001b[1;32m    148\u001b[0m         \"\"\"\n\u001b[1;32m    149\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mSequence\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/stable_baselines3/common/vec_env/dummy_vec_env.py\u001b[0m in \u001b[0;36mstep_wait\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     42\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0menv_idx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_envs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             obs, self.buf_rews[env_idx], self.buf_dones[env_idx], self.buf_infos[env_idx] = self.envs[env_idx].step(\n\u001b[0;32m---> 44\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0menv_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m             )\n\u001b[1;32m     46\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuf_dones\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0menv_idx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/finrl/env/env_stocktrading.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self, actions)\u001b[0m\n\u001b[1;32m    253\u001b[0m             \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstock_dim\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstock_dim\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstock_dim\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masset_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_total_asset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_date\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    256\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreward\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mend_total_asset\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbegin_total_asset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrewards_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreward\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/finrl/env/env_stocktrading.py\u001b[0m in \u001b[0;36m_get_date\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    338\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_date\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 340\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    341\u001b[0m             \u001b[0mdate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    342\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36munique\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1879\u001b[0m         \u001b[0mCategories\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'a'\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m'b'\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1880\u001b[0m         \"\"\"\n\u001b[0;32m-> 1881\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1882\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1883\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/base.py\u001b[0m in \u001b[0;36munique\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1263\u001b[0m                     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1264\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1265\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munique1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1266\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1267\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/algorithms.py\u001b[0m in \u001b[0;36munique\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m    394\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    395\u001b[0m     \u001b[0moriginal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 396\u001b[0;31m     \u001b[0mhtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_hashtable_algo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    397\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    398\u001b[0m     \u001b[0mtable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhtable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/algorithms.py\u001b[0m in \u001b[0;36m_get_hashtable_algo\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m    258\u001b[0m     \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ensure_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 260\u001b[0;31m     \u001b[0mndtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_object_for_strings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    261\u001b[0m     \u001b[0mhtable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_hashtables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mndtype\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    262\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mhtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/algorithms.py\u001b[0m in \u001b[0;36m_check_object_for_strings\u001b[0;34m(values)\u001b[0m\n\u001b[1;32m    299\u001b[0m         \u001b[0;31m# including nulls because that is the only difference between\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    300\u001b[0m         \u001b[0;31m# StringHashTable and ObjectHashtable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 301\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfer_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"string\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    302\u001b[0m             \u001b[0mndtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"string\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    303\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mndtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Zpv4S0-fDBv"
      },
      "source": [
        "### Model 4: TD3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JSAHhV4Xc-bh",
        "outputId": "e531db14-aab4-47d1-cc15-02c893ec66c9"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "TD3_PARAMS = {\"batch_size\": 100, \n",
        "              \"buffer_size\": 1000000, \n",
        "              \"learning_rate\": 0.001}\n",
        "\n",
        "model_td3 = agent.get_model(\"td3\",model_kwargs = TD3_PARAMS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 100, 'buffer_size': 1000000, 'learning_rate': 0.001}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSRxNYAxdKpU",
        "outputId": "ddc4193c-884b-4a2c-9e49-31397e2cfbec"
      },
      "source": [
        "trained_td3 = agent.train_model(model=model_td3, \n",
        "                             tb_log_name='td3',\n",
        "                             total_timesteps=30000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/td3/td3_2\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 4        |\n",
            "|    fps             | 33       |\n",
            "|    time_elapsed    | 296      |\n",
            "|    total timesteps | 10064    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 67.9     |\n",
            "|    critic_loss     | 979      |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 7548     |\n",
            "---------------------------------\n",
            "day: 2515, episode: 10\n",
            "begin_total_asset:1000000.00\n",
            "end_total_asset:4438572.29\n",
            "total_reward:3438572.29\n",
            "total_cost: 1038.05\n",
            "total_trades: 40290\n",
            "Sharpe: 1.049\n",
            "=================================\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 8        |\n",
            "|    fps             | 30       |\n",
            "|    time_elapsed    | 669      |\n",
            "|    total timesteps | 20128    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 54       |\n",
            "|    critic_loss     | 199      |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 17612    |\n",
            "---------------------------------\n",
            "---------------------------------\n",
            "| time/              |          |\n",
            "|    episodes        | 12       |\n",
            "|    fps             | 28       |\n",
            "|    time_elapsed    | 1052     |\n",
            "|    total timesteps | 30192    |\n",
            "| train/             |          |\n",
            "|    actor_loss      | 41.4     |\n",
            "|    critic_loss     | 25.2     |\n",
            "|    learning_rate   | 0.001    |\n",
            "|    n_updates       | 27676    |\n",
            "---------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dr49PotrfG01"
      },
      "source": [
        "### Model 5: SAC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwOhVjqRkCdM",
        "outputId": "44fcd5ee-7ce3-4c21-b2c8-131b7bba8127"
      },
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "SAC_PARAMS = {\n",
        "    \"batch_size\": 128,\n",
        "    \"buffer_size\": 1000000,\n",
        "    \"learning_rate\": 0.0001,\n",
        "    \"learning_starts\": 100,\n",
        "    \"ent_coef\": \"auto_0.1\",\n",
        "}\n",
        "\n",
        "model_sac = agent.get_model(\"sac\",model_kwargs = SAC_PARAMS)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'batch_size': 128, 'buffer_size': 1000000, 'learning_rate': 0.0001, 'learning_starts': 100, 'ent_coef': 'auto_0.1'}\n",
            "Using cpu device\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K8RSdKCckJyH",
        "outputId": "89e2ae3c-2559-4f93-82d4-168f9853db9e"
      },
      "source": [
        "trained_sac = agent.train_model(model=model_sac, \n",
        "                             tb_log_name='sac',\n",
        "                             total_timesteps=80000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Logging to tensorboard_log/sac/sac_1\n",
            "day: 2515, episode: 10\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3458901.03\n",
            "total_reward: 2458901.03\n",
            "total_cost: 184848.78\n",
            "total_trades: 59497\n",
            "Sharpe: 0.795\n",
            "=================================\n",
            "----------------------------------\n",
            "| environment/        |          |\n",
            "|    portfolio_value  | 3.46e+06 |\n",
            "|    total_cost       | 1.85e+05 |\n",
            "|    total_reward     | 2.46e+06 |\n",
            "|    total_reward_pct | 246      |\n",
            "|    total_trades     | 59497    |\n",
            "| time/               |          |\n",
            "|    episodes         | 4        |\n",
            "|    fps              | 25       |\n",
            "|    time_elapsed     | 390      |\n",
            "|    total timesteps  | 10064    |\n",
            "| train/              |          |\n",
            "|    actor_loss       | 802      |\n",
            "|    critic_loss      | 561      |\n",
            "|    ent_coef         | 0.217    |\n",
            "|    ent_coef_loss    | -37.2    |\n",
            "|    learning_rate    | 0.0001   |\n",
            "|    n_updates        | 9963     |\n",
            "----------------------------------\n",
            "----------------------------------\n",
            "| environment/        |          |\n",
            "|    portfolio_value  | 4.59e+06 |\n",
            "|    total_cost       | 9.06e+04 |\n",
            "|    total_reward     | 3.59e+06 |\n",
            "|    total_reward_pct | 359      |\n",
            "|    total_trades     | 56336    |\n",
            "| time/               |          |\n",
            "|    episodes         | 8        |\n",
            "|    fps              | 25       |\n",
            "|    time_elapsed     | 789      |\n",
            "|    total timesteps  | 20128    |\n",
            "| train/              |          |\n",
            "|    actor_loss       | 347      |\n",
            "|    critic_loss      | 47.3     |\n",
            "|    ent_coef         | 0.0813   |\n",
            "|    ent_coef_loss    | -121     |\n",
            "|    learning_rate    | 0.0001   |\n",
            "|    n_updates        | 20027    |\n",
            "----------------------------------\n",
            "----------------------------------\n",
            "| environment/        |          |\n",
            "|    portfolio_value  | 4.21e+06 |\n",
            "|    total_cost       | 5.02e+04 |\n",
            "|    total_reward     | 3.21e+06 |\n",
            "|    total_reward_pct | 321      |\n",
            "|    total_trades     | 55078    |\n",
            "| time/               |          |\n",
            "|    episodes         | 12       |\n",
            "|    fps              | 25       |\n",
            "|    time_elapsed     | 1191     |\n",
            "|    total timesteps  | 30192    |\n",
            "| train/              |          |\n",
            "|    actor_loss       | 190      |\n",
            "|    critic_loss      | 61.6     |\n",
            "|    ent_coef         | 0.0298   |\n",
            "|    ent_coef_loss    | -162     |\n",
            "|    learning_rate    | 0.0001   |\n",
            "|    n_updates        | 30091    |\n",
            "----------------------------------\n",
            "day: 2515, episode: 20\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 5938251.09\n",
            "total_reward: 4938251.09\n",
            "total_cost: 28977.47\n",
            "total_trades: 53990\n",
            "Sharpe: 1.047\n",
            "=================================\n",
            "----------------------------------\n",
            "| environment/        |          |\n",
            "|    portfolio_value  | 6.11e+06 |\n",
            "|    total_cost       | 1.35e+04 |\n",
            "|    total_reward     | 5.11e+06 |\n",
            "|    total_reward_pct | 511      |\n",
            "|    total_trades     | 52631    |\n",
            "| time/               |          |\n",
            "|    episodes         | 16       |\n",
            "|    fps              | 25       |\n",
            "|    time_elapsed     | 1595     |\n",
            "|    total timesteps  | 40256    |\n",
            "| train/              |          |\n",
            "|    actor_loss       | 105      |\n",
            "|    critic_loss      | 34.7     |\n",
            "|    ent_coef         | 0.011    |\n",
            "|    ent_coef_loss    | -185     |\n",
            "|    learning_rate    | 0.0001   |\n",
            "|    n_updates        | 40155    |\n",
            "----------------------------------\n",
            "----------------------------------\n",
            "| environment/        |          |\n",
            "|    portfolio_value  | 5.55e+06 |\n",
            "|    total_cost       | 5.24e+03 |\n",
            "|    total_reward     | 4.55e+06 |\n",
            "|    total_reward_pct | 455      |\n",
            "|    total_trades     | 51269    |\n",
            "| time/               |          |\n",
            "|    episodes         | 20       |\n",
            "|    fps              | 25       |\n",
            "|    time_elapsed     | 2002     |\n",
            "|    total timesteps  | 50320    |\n",
            "| train/              |          |\n",
            "|    actor_loss       | 60.1     |\n",
            "|    critic_loss      | 13.5     |\n",
            "|    ent_coef         | 0.00413  |\n",
            "|    ent_coef_loss    | -174     |\n",
            "|    learning_rate    | 0.0001   |\n",
            "|    n_updates        | 50219    |\n",
            "----------------------------------\n",
            "day: 2515, episode: 30\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 5343010.26\n",
            "total_reward: 4343010.26\n",
            "total_cost: 3151.20\n",
            "total_trades: 49562\n",
            "Sharpe: 1.017\n",
            "=================================\n",
            "----------------------------------\n",
            "| environment/        |          |\n",
            "|    portfolio_value  | 5.34e+06 |\n",
            "|    total_cost       | 3.15e+03 |\n",
            "|    total_reward     | 4.34e+06 |\n",
            "|    total_reward_pct | 434      |\n",
            "|    total_trades     | 49562    |\n",
            "| time/               |          |\n",
            "|    episodes         | 24       |\n",
            "|    fps              | 25       |\n",
            "|    time_elapsed     | 2410     |\n",
            "|    total timesteps  | 60384    |\n",
            "| train/              |          |\n",
            "|    actor_loss       | 34.1     |\n",
            "|    critic_loss      | 8.66     |\n",
            "|    ent_coef         | 0.00161  |\n",
            "|    ent_coef_loss    | -103     |\n",
            "|    learning_rate    | 0.0001   |\n",
            "|    n_updates        | 60283    |\n",
            "----------------------------------\n",
            "----------------------------------\n",
            "| environment/        |          |\n",
            "|    portfolio_value  | 4.67e+06 |\n",
            "|    total_cost       | 2.3e+03  |\n",
            "|    total_reward     | 3.67e+06 |\n",
            "|    total_reward_pct | 367      |\n",
            "|    total_trades     | 46109    |\n",
            "| time/               |          |\n",
            "|    episodes         | 28       |\n",
            "|    fps              | 24       |\n",
            "|    time_elapsed     | 2824     |\n",
            "|    total timesteps  | 70448    |\n",
            "| train/              |          |\n",
            "|    actor_loss       | 23.3     |\n",
            "|    critic_loss      | 10.3     |\n",
            "|    ent_coef         | 0.000899 |\n",
            "|    ent_coef_loss    | 1.3      |\n",
            "|    learning_rate    | 0.0001   |\n",
            "|    n_updates        | 70347    |\n",
            "----------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2wZgkQXh1jE"
      },
      "source": [
        "## Trading\n",
        "Assume that we have $1,000,000 initial capital at 2019-01-01. We use the DDPG model to trade Dow jones 30 stocks."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bEv5KGC8h1jE"
      },
      "source": [
        "### Set turbulence threshold\n",
        "Set the turbulence threshold to be greater than the maximum of insample turbulence data, if current turbulence index is greater than the threshold, then we assume that the current market is volatile"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efwBi84ch1jE"
      },
      "source": [
        "data_turbulence = processed_full[(processed_full.date<'2019-01-01') & (processed_full.date>='2009-01-01')]\n",
        "insample_turbulence = data_turbulence.drop_duplicates(subset=['date'])"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VHZMBpSqh1jG",
        "outputId": "6dd6e5d6-38d1-4f60-ae70-1168757eb1fc"
      },
      "source": [
        "insample_turbulence.turbulence.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    2516.000000\n",
              "mean       33.278197\n",
              "std        33.999888\n",
              "min         0.000000\n",
              "25%        15.233650\n",
              "50%        25.166725\n",
              "75%        39.289944\n",
              "max       332.850050\n",
              "Name: turbulence, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yuwDPkV9h1jL"
      },
      "source": [
        "turbulence_threshold = np.quantile(insample_turbulence.turbulence.values,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wwoz_7VSh1jO",
        "outputId": "a22d3e00-8a19-4b18-dd3e-9d072a0f6aca"
      },
      "source": [
        "turbulence_threshold"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "332.8500501393391"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U5mmgQF_h1jQ"
      },
      "source": [
        "### Trade\n",
        "\n",
        "DRL model needs to update periodically in order to take full advantage of the data, ideally we need to retrain our model yearly, quarterly, or monthly. We also need to tune the parameters along the way, in this notebook I only use the in-sample data from 2009-01 to 2018-12 to tune the parameters once, so there is some alpha decay here as the length of trade date extends. \n",
        "\n",
        "Numerous hyperparameters – e.g. the learning rate, the total number of samples to train on – influence the learning process and are usually determined by testing some variations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cIqoV0GSI52v"
      },
      "source": [
        "trade = data_split(processed_full, '2020-07-01','2021-07-06')\n",
        "e_trade_gym = StockTradingEnv(df = trade, turbulence_threshold = 380, **env_kwargs)\n",
        "# env_trade, obs_trade = e_trade_gym.get_sb_env()"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "W_XNgGsBMeVw",
        "outputId": "478b18bc-1335-401d-fb8b-f781a8ef5ccb"
      },
      "source": [
        "trade.head()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>day</th>\n",
              "      <th>macd</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>turbulence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-07-01</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>91.279999</td>\n",
              "      <td>91.839996</td>\n",
              "      <td>90.977501</td>\n",
              "      <td>90.418251</td>\n",
              "      <td>110737200.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.031827</td>\n",
              "      <td>62.807148</td>\n",
              "      <td>107.482905</td>\n",
              "      <td>29.730532</td>\n",
              "      <td>23.958878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-07-01</td>\n",
              "      <td>AXP</td>\n",
              "      <td>95.250000</td>\n",
              "      <td>96.959999</td>\n",
              "      <td>93.639999</td>\n",
              "      <td>92.809692</td>\n",
              "      <td>3301000.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.392220</td>\n",
              "      <td>48.504814</td>\n",
              "      <td>-66.344801</td>\n",
              "      <td>3.142448</td>\n",
              "      <td>23.958878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-07-01</td>\n",
              "      <td>BA</td>\n",
              "      <td>185.880005</td>\n",
              "      <td>190.610001</td>\n",
              "      <td>180.039993</td>\n",
              "      <td>180.320007</td>\n",
              "      <td>49036700.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.443193</td>\n",
              "      <td>50.925771</td>\n",
              "      <td>24.220608</td>\n",
              "      <td>15.932920</td>\n",
              "      <td>23.958878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-07-01</td>\n",
              "      <td>CAT</td>\n",
              "      <td>129.380005</td>\n",
              "      <td>129.399994</td>\n",
              "      <td>125.879997</td>\n",
              "      <td>123.148453</td>\n",
              "      <td>2807800.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.298965</td>\n",
              "      <td>52.865417</td>\n",
              "      <td>35.489858</td>\n",
              "      <td>14.457404</td>\n",
              "      <td>23.958878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-07-01</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>46.540001</td>\n",
              "      <td>46.720001</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>44.289536</td>\n",
              "      <td>17129500.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.277462</td>\n",
              "      <td>53.160557</td>\n",
              "      <td>18.936292</td>\n",
              "      <td>13.380576</td>\n",
              "      <td>23.958878</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date   tic        open  ...      cci_30      dx_30  turbulence\n",
              "0  2020-07-01  AAPL   91.279999  ...  107.482905  29.730532   23.958878\n",
              "0  2020-07-01   AXP   95.250000  ...  -66.344801   3.142448   23.958878\n",
              "0  2020-07-01    BA  185.880005  ...   24.220608  15.932920   23.958878\n",
              "0  2020-07-01   CAT  129.380005  ...   35.489858  14.457404   23.958878\n",
              "0  2020-07-01  CSCO   46.540001  ...   18.936292  13.380576   23.958878\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eLOnL5eYh1jR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4753f1f-abbc-48db-ace8-2dfce9acaaee"
      },
      "source": [
        "df_account_value, df_actions = DRLAgent.DRL_prediction(\n",
        "    model=trained_a2c, \n",
        "    environment = e_trade_gym)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "hit end!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ERxw3KqLkcP4",
        "outputId": "23605410-7c0c-451c-d6bc-1db5268cc876"
      },
      "source": [
        "df_account_value.shape"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(254, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "2yRkNguY5yvp",
        "outputId": "0fc69fc2-ba55-4777-d35b-a73b114e6dbd"
      },
      "source": [
        "df_account_value.tail()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>account_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>2021-06-28</td>\n",
              "      <td>1.460759e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>250</th>\n",
              "      <td>2021-06-29</td>\n",
              "      <td>1.456598e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>251</th>\n",
              "      <td>2021-06-30</td>\n",
              "      <td>1.466562e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>252</th>\n",
              "      <td>2021-07-01</td>\n",
              "      <td>1.467280e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>253</th>\n",
              "      <td>2021-07-02</td>\n",
              "      <td>1.469805e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           date  account_value\n",
              "249  2021-06-28   1.460759e+06\n",
              "250  2021-06-29   1.456598e+06\n",
              "251  2021-06-30   1.466562e+06\n",
              "252  2021-07-01   1.467280e+06\n",
              "253  2021-07-02   1.469805e+06"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "nFlK5hNbWVFk",
        "outputId": "c4246e2c-f3da-475c-f077-f5f5cd9756aa"
      },
      "source": [
        "df_actions.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AAPL</th>\n",
              "      <th>AXP</th>\n",
              "      <th>BA</th>\n",
              "      <th>CAT</th>\n",
              "      <th>CSCO</th>\n",
              "      <th>CVX</th>\n",
              "      <th>DD</th>\n",
              "      <th>DIS</th>\n",
              "      <th>GS</th>\n",
              "      <th>HD</th>\n",
              "      <th>IBM</th>\n",
              "      <th>INTC</th>\n",
              "      <th>JNJ</th>\n",
              "      <th>JPM</th>\n",
              "      <th>KO</th>\n",
              "      <th>MCD</th>\n",
              "      <th>MMM</th>\n",
              "      <th>MRK</th>\n",
              "      <th>MSFT</th>\n",
              "      <th>NKE</th>\n",
              "      <th>PFE</th>\n",
              "      <th>PG</th>\n",
              "      <th>RTX</th>\n",
              "      <th>TRV</th>\n",
              "      <th>UNH</th>\n",
              "      <th>V</th>\n",
              "      <th>VZ</th>\n",
              "      <th>WBA</th>\n",
              "      <th>WMT</th>\n",
              "      <th>XOM</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-07-01</th>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>49</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>39</td>\n",
              "      <td>0</td>\n",
              "      <td>98</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>87</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-02</th>\n",
              "      <td>0</td>\n",
              "      <td>89</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>87</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>92</td>\n",
              "      <td>54</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>-24</td>\n",
              "      <td>-4</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>32</td>\n",
              "      <td>-26</td>\n",
              "      <td>0</td>\n",
              "      <td>87</td>\n",
              "      <td>73</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-06</th>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>-31</td>\n",
              "      <td>100</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "      <td>89</td>\n",
              "      <td>-2</td>\n",
              "      <td>-3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-24</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>-15</td>\n",
              "      <td>100</td>\n",
              "      <td>33</td>\n",
              "      <td>84</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-07</th>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>75</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>50</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>29</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>-100</td>\n",
              "      <td>7</td>\n",
              "      <td>36</td>\n",
              "      <td>-22</td>\n",
              "      <td>-8</td>\n",
              "      <td>100</td>\n",
              "      <td>-100</td>\n",
              "      <td>-100</td>\n",
              "      <td>-96</td>\n",
              "      <td>75</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-08</th>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>-43</td>\n",
              "      <td>100</td>\n",
              "      <td>43</td>\n",
              "      <td>33</td>\n",
              "      <td>0</td>\n",
              "      <td>88</td>\n",
              "      <td>15</td>\n",
              "      <td>100</td>\n",
              "      <td>-100</td>\n",
              "      <td>82</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>43</td>\n",
              "      <td>100</td>\n",
              "      <td>65</td>\n",
              "      <td>-36</td>\n",
              "      <td>100</td>\n",
              "      <td>-38</td>\n",
              "      <td>8</td>\n",
              "      <td>100</td>\n",
              "      <td>44</td>\n",
              "      <td>-37</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            AAPL  AXP  BA  CAT  CSCO  CVX  ...  UNH    V   VZ  WBA  WMT  XOM\n",
              "date                                       ...                              \n",
              "2020-07-01     0  100   0    0   100    0  ...    0   98    0    0   87    0\n",
              "2020-07-02     0   89   0  100   100   87  ...    0   87   73  100  100    0\n",
              "2020-07-06     0   11   0  -31   100    1  ...    0  -15  100   33   84    0\n",
              "2020-07-07     0   30  75  100   100   60  ...  100 -100 -100  -96   75    0\n",
              "2020-07-08     0    7 -43  100    43   33  ...    8  100   44  -37  100    0\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6vvNSC6h1jZ"
      },
      "source": [
        "<a id='6'></a>\n",
        "# Part 7: Backtest Our Strategy\n",
        "Backtesting plays a key role in evaluating the performance of a trading strategy. Automated backtesting tool is preferred because it reduces the human error. We usually use the Quantopian pyfolio package to backtest our trading strategies. It is easy to use and consists of various individual plots that provide a comprehensive image of the performance of a trading strategy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lr2zX7ZxNyFQ"
      },
      "source": [
        "<a id='6.1'></a>\n",
        "## 7.1 BackTestStats\n",
        "pass in df_account_value, this information is stored in env class\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nzkr9yv-AdV_",
        "outputId": "cac305a5-28cc-4aca-f2d8-09c6c2f5a340"
      },
      "source": [
        "print(\"==============Get Backtest Results===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "\n",
        "perf_stats_all = backtest_stats(account_value=df_account_value)\n",
        "perf_stats_all = pd.DataFrame(perf_stats_all)\n",
        "perf_stats_all.to_csv(\"./\"+config.RESULTS_DIR+\"/perf_stats_all_\"+now+'.csv')"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Backtest Results===========\n",
            "Annual return          0.465354\n",
            "Cumulative returns     0.469805\n",
            "Annual volatility      0.177943\n",
            "Sharpe ratio           2.246005\n",
            "Calmar ratio           6.130722\n",
            "Stability              0.937336\n",
            "Max drawdown          -0.075905\n",
            "Omega ratio            1.433423\n",
            "Sortino ratio          3.500963\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             1.200493\n",
            "Daily value at risk   -0.020833\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QkV-LB66iwhD",
        "outputId": "408cc33f-199e-4b27-eafa-eb6abf72f1e5"
      },
      "source": [
        "#baseline stats\n",
        "print(\"==============Get Baseline Stats===========\")\n",
        "baseline_df = get_baseline(\n",
        "        ticker=\"^DJI\", \n",
        "        start = df_account_value.loc[0,'date'],\n",
        "        end = df_account_value.loc[len(df_account_value)-1,'date'])\n",
        "\n",
        "stats = backtest_stats(baseline_df, value_col_name = 'close')\n"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Baseline Stats===========\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (253, 8)\n",
            "Annual return          0.344198\n",
            "Cumulative returns     0.345777\n",
            "Annual volatility      0.145557\n",
            "Sharpe ratio           2.114021\n",
            "Calmar ratio           3.854069\n",
            "Stability              0.945897\n",
            "Max drawdown          -0.089308\n",
            "Omega ratio            1.421717\n",
            "Sortino ratio          3.165889\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             1.081322\n",
            "Daily value at risk   -0.017117\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "qg1kvfemrrQH",
        "outputId": "2bebcd37-d003-485a-da11-ba4ee2ba57f5"
      },
      "source": [
        "df_account_value.loc[0,'date']"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2020-07-01'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tt1bzL5OrsTa"
      },
      "source": [
        "df_account_value.loc[len(df_account_value)-1,'date']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9U6Suru3h1jc"
      },
      "source": [
        "<a id='6.2'></a>\n",
        "## 7.2 BackTestPlot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lKRGftSS7pNM",
        "outputId": "11578779-2298-4fe9-8f0f-1fef345046a7"
      },
      "source": [
        "print(\"==============Compare to DJIA===========\")\n",
        "%matplotlib inline\n",
        "# S&P 500: ^GSPC\n",
        "# Dow Jones Index: ^DJI\n",
        "# NASDAQ 100: ^NDX\n",
        "backtest_plot(df_account_value, \n",
        "             baseline_ticker = '^DJI', \n",
        "             baseline_start = df_account_value.loc[0,'date'],\n",
        "             baseline_end = df_account_value.loc[len(df_account_value)-1,'date'])"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Compare to DJIA===========\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (253, 8)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2020-07-01</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2021-07-01</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>12</td></tr>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Backtest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Annual return</th>\n",
              "      <td>46.506%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>46.728%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>17.83%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>2.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>6.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-7.591%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>3.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>1.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-2.088%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>1.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Worst drawdown periods</th>\n",
              "      <th>Net drawdown in %</th>\n",
              "      <th>Peak date</th>\n",
              "      <th>Valley date</th>\n",
              "      <th>Recovery date</th>\n",
              "      <th>Duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.59</td>\n",
              "      <td>2020-10-16</td>\n",
              "      <td>2020-10-28</td>\n",
              "      <td>2020-11-09</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.32</td>\n",
              "      <td>2021-05-17</td>\n",
              "      <td>2021-06-18</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.22</td>\n",
              "      <td>2020-09-02</td>\n",
              "      <td>2020-09-23</td>\n",
              "      <td>2020-10-12</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5.17</td>\n",
              "      <td>2021-03-17</td>\n",
              "      <td>2021-03-23</td>\n",
              "      <td>2021-04-16</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.01</td>\n",
              "      <td>2021-01-14</td>\n",
              "      <td>2021-01-29</td>\n",
              "      <td>2021-02-05</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
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              "<IPython.core.display.HTML object>"
            ]
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pyfolio/tears.py:907: UserWarning: Passed returns do not overlap with anyinteresting times.\n",
            "  'interesting times.', UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x5184 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BzBaE63H3RLc"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}